Initial merged FP16 model commit

README.md

@@ -17,105 +17,17 @@ license: other
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-# Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K GPTQ
+# Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K fp16
 
-These files are GPTQ 4bit model files for [Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test).
+These files are pytorch format fp16 model files for [Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test).
 
-It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+It is the result of merging and/or converting the source repository to float16.
 
 ## Repositories available
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Superhot-8K-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/none)
-* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored)
-
-## How to easily download and use this model in text-generation-webui
-
-Please make sure you're using the latest version of text-generation-webui
-
-1. Click the **Model tab**.
-2. Under **Download custom model or LoRA**, enter `TheBloke/Wizard-Vicuna-30B-Superhot-8K-GPTQ`.
-3. Click **Download**.
-4. The model will start downloading. Once it's finished it will say "Done"
-5. In the top left, click the refresh icon next to **Model**.
-6. In the **Model** dropdown, choose the model you just downloaded: `Wizard-Vicuna-30B-Superhot-8K-GPTQ`
-7. The model will automatically load, and is now ready for use!
-8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
-  * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
-9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
-
-## How to use this GPTQ model from Python code
-
-First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
-
-`pip install auto-gptq`
-
-Then try the following example code:
-
-```python
-from transformers import AutoTokenizer, pipeline, logging
-from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
-import argparse
-
-model_name_or_path = "TheBloke/Wizard-Vicuna-30B-Superhot-8K-GPTQ"
-model_basename = "wizard-vicuna-30b-superhot-8k-GPTQ-4bit--1g.act.order"
-
-use_triton = False
-
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
-
-model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
-        model_basename=model_basename,
-        use_safetensors=True,
-        trust_remote_code=False,
-        device="cuda:0",
-        use_triton=use_triton,
-        quantize_config=None)
-
-# Note: check the prompt template is correct for this model.
-prompt = "Tell me about AI"
-prompt_template=f'''USER: {prompt}
-ASSISTANT:'''
-
-print("\n\n*** Generate:")
-
-input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
-output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
-print(tokenizer.decode(output[0]))
-
-# Inference can also be done using transformers' pipeline
-
-# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
-logging.set_verbosity(logging.CRITICAL)
-
-print("*** Pipeline:")
-pipe = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_new_tokens=512,
-    temperature=0.7,
-    top_p=0.95,
-    repetition_penalty=1.15
-)
-
-print(pipe(prompt_template)[0]['generated_text'])
-```
-
-## Provided files
-
-**wizard-vicuna-30b-superhot-8k-GPTQ-4bit--1g.act.order.safetensors**
-
-This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.
-
-It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.
-
-* `wizard-vicuna-30b-superhot-8k-GPTQ-4bit--1g.act.order.safetensors`
-  * Works with AutoGPTQ in CUDA or Triton modes.
-  * LLaMa models also work with [ExLlama](https://github.com/turboderp/exllama}, which usually provides much higher performance, and uses less VRAM, than AutoGPTQ.
-  * Works with GPTQ-for-LLaMa in CUDA mode.  May have issues with GPTQ-for-LLaMa Triton mode.
-  * Works with text-generation-webui, including one-click-installers.
-  * Parameters: Groupsize = -1. Act Order / desc_act = True.
+* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Superhot-8K-GPTQ)
 
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